In this course project I trained a classifier to identify type of a particle. There are six particle types: electron, proton, muon, kaon, pion and ghost. Ghost is a particle with other type than the first five or a detector noise. Different particle types remain different responses in the detector systems or subdetectors. Thre are five systems: tracking system, ring imaging Cherenkov detector (RICH), electromagnetic and hadron calorimeters, and muon system. Now, the main task is to identify a particle type using the responses in the detector systems. Another thing to note down is- the traning dataset is bigger than 50MB that's why i was not able to upload it on github; one can download it form just searching the topic mentioned below.
I have done the above course project during my course-work "Artificial Intelligence(DSE313)" while studying at Indian Institute of Science Educatioon and Research(IISER) Bhopal in the 3rd year of my BS in Data Science. In this course project my teammate was Saswata Sarkar, he has done the Keras part and rest of the code including data preprocessing and SKlearnClassifier was done by me.
This project was inspired by the course in Coursera, mentioned here- "Addressing Large Hadron Collider Challenges by Machine Learning".